



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DetrImageProcessor



class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __init__(							self      :  List[str]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  List[Any]=7						, __UpperCamelCase      :  Dict=3						, __UpperCamelCase      :  str=30						, __UpperCamelCase      :  int=400						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  Optional[Any]=None						, __UpperCamelCase      :  int=True						, __UpperCamelCase      :  Optional[Any]=1 / 255						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  Any=[0.5, 0.5, 0.5]						, __UpperCamelCase      :  Tuple=[0.5, 0.5, 0.5]						, __UpperCamelCase      :  Tuple=True						, )		->     List[Any]:
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											A      						=    size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
											A      						=    parent
											A      						=    batch_size
											A      						=    num_channels
											A      						=    min_resolution
											A      						=    max_resolution
											A      						=    do_resize
											A      						=    size
											A      						=    do_rescale
											A      						=    rescale_factor
											A      						=    do_normalize
											A      						=    image_mean
											A      						=    image_std
											A      						=    do_pad


				def __UpperCamelCase						(							self      :  List[str]       )		->     str:
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_pad": self.do_pad,
											}


				def __UpperCamelCase						(							self      :  Any						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  Dict=False       )		->     Union[str, Any]:
											if not batched:
																		A      						=    image_inputs[0]
																		if isinstance(__UpperCamelCase						, Image.Image       ):
																									A						,   A      						=    image.size
																		else:
																									A						,   A      						=    image.shape[1], image.shape[2]
																		if w < h:
																									A      						=    int(self.size['shortest_edge'] * h / w       )
																									A      						=    self.size['shortest_edge']
																		elif w > h:
																									A      						=    self.size['shortest_edge']
																									A      						=    int(self.size['shortest_edge'] * w / h       )
																		else:
																									A      						=    self.size['shortest_edge']
																									A      						=    self.size['shortest_edge']

											else:
																		A      						=    []
																		for image in image_inputs:
																									A						,   A      						=    self.get_expected_values([image]       )
																									expected_values.append((expected_height, expected_width)       )
																		A      						=    max(__UpperCamelCase						, key=lambda __UpperCamelCase       : item[0]       )[0]
																		A      						=    max(__UpperCamelCase						, key=lambda __UpperCamelCase       : item[1]       )[1]

											return expected_height, expected_width





@require_torch
@require_vision
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     int								=     DetrImageProcessor if is_vision_available() else None


				def __UpperCamelCase						(							self      :  int       )		->     Any:
											A      						=    DetrImageProcessingTester(self       )


				@property
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[str]:
											return self.image_processor_tester.prepare_image_processor_dict()


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    self.image_processing_class(**self.image_processor_dict       )
											self.assertTrue(hasattr(__UpperCamelCase						, 'image_mean'       )       )
											self.assertTrue(hasattr(__UpperCamelCase						, 'image_std'       )       )
											self.assertTrue(hasattr(__UpperCamelCase						, 'do_normalize'       )       )
											self.assertTrue(hasattr(__UpperCamelCase						, 'do_rescale'       )       )
											self.assertTrue(hasattr(__UpperCamelCase						, 'rescale_factor'       )       )
											self.assertTrue(hasattr(__UpperCamelCase						, 'do_resize'       )       )
											self.assertTrue(hasattr(__UpperCamelCase						, 'size'       )       )
											self.assertTrue(hasattr(__UpperCamelCase						, 'do_pad'       )       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Any:
											A      						=    self.image_processing_class.from_dict(self.image_processor_dict       )
											self.assertEqual(image_processor.size						, {'shortest_edge': 18, 'longest_edge': 1_333}       )
											self.assertEqual(image_processor.do_pad						, __UpperCamelCase       )

											A      						=    self.image_processing_class.from_dict(
											    self.image_processor_dict						, size=42						, max_size=84						, pad_and_return_pixel_mask=__UpperCamelCase       )
											self.assertEqual(image_processor.size						, {'shortest_edge': 42, 'longest_edge': 84}       )
											self.assertEqual(image_processor.do_pad						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Optional[Any]:
											pass


				def __UpperCamelCase						(							self      :  List[Any]       )		->     str:
											# Initialize image_processing
											A      						=    self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											A      						=    prepare_image_inputs(self.image_processor_tester						, equal_resolution=__UpperCamelCase       )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCamelCase						, Image.Image       )

											# Test not batched input
											A      						=    image_processing(image_inputs[0]						, return_tensors='pt'       ).pixel_values

											A						,   A      						=    self.image_processor_tester.get_expected_values(__UpperCamelCase       )

											self.assertEqual(
											    encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

											# Test batched
											A						,   A      						=    self.image_processor_tester.get_expected_values(__UpperCamelCase						, batched=__UpperCamelCase       )

											A      						=    image_processing(__UpperCamelCase						, return_tensors='pt'       ).pixel_values
											self.assertEqual(
											    encoded_images.shape						, (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						, )


				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[int]:
											# Initialize image_processing
											A      						=    self.image_processing_class(**self.image_processor_dict       )
											# create random numpy tensors
											A      						=    prepare_image_inputs(self.image_processor_tester						, equal_resolution=__UpperCamelCase						, numpify=__UpperCamelCase       )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCamelCase						, np.ndarray       )

											# Test not batched input
											A      						=    image_processing(image_inputs[0]						, return_tensors='pt'       ).pixel_values

											A						,   A      						=    self.image_processor_tester.get_expected_values(__UpperCamelCase       )

											self.assertEqual(
											    encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

											# Test batched
											A      						=    image_processing(__UpperCamelCase						, return_tensors='pt'       ).pixel_values

											A						,   A      						=    self.image_processor_tester.get_expected_values(__UpperCamelCase						, batched=__UpperCamelCase       )

											self.assertEqual(
											    encoded_images.shape						, (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						, )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											# Initialize image_processing
											A      						=    self.image_processing_class(**self.image_processor_dict       )
											# create random PyTorch tensors
											A      						=    prepare_image_inputs(self.image_processor_tester						, equal_resolution=__UpperCamelCase						, torchify=__UpperCamelCase       )
											for image in image_inputs:
																		self.assertIsInstance(__UpperCamelCase						, torch.Tensor       )

											# Test not batched input
											A      						=    image_processing(image_inputs[0]						, return_tensors='pt'       ).pixel_values

											A						,   A      						=    self.image_processor_tester.get_expected_values(__UpperCamelCase       )

											self.assertEqual(
											    encoded_images.shape						, (1, self.image_processor_tester.num_channels, expected_height, expected_width)						, )

											# Test batched
											A      						=    image_processing(__UpperCamelCase						, return_tensors='pt'       ).pixel_values

											A						,   A      						=    self.image_processor_tester.get_expected_values(__UpperCamelCase						, batched=__UpperCamelCase       )

											self.assertEqual(
											    encoded_images.shape						, (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						, )


				@slow
				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											# prepare image and target
											A      						=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
											with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						, 'r'       ) as f:
																		A      						=    json.loads(f.read()       )

											A      						=    {'image_id': 39_769, 'annotations': target}

											# encode them
											A      						=    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'       )
											A      						=    image_processing(images=__UpperCamelCase						, annotations=__UpperCamelCase						, return_tensors='pt'       )

											# verify pixel values
											A      						=    torch.Size([1, 3, 800, 1_066]       )
											self.assertEqual(encoding['pixel_values'].shape						, __UpperCamelCase       )

											A      						=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						, __UpperCamelCase						, atol=1e-4       )       )

											# verify area
											A      						=    torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']						, __UpperCamelCase       )       )
											# verify boxes
											A      						=    torch.Size([6, 4]       )
											self.assertEqual(encoding['labels'][0]['boxes'].shape						, __UpperCamelCase       )
											A      						=    torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						, __UpperCamelCase						, atol=1e-3       )       )
											# verify image_id
											A      						=    torch.tensor([39_769]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						, __UpperCamelCase       )       )
											# verify is_crowd
											A      						=    torch.tensor([0, 0, 0, 0, 0, 0]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						, __UpperCamelCase       )       )
											# verify class_labels
											A      						=    torch.tensor([75, 75, 63, 65, 17, 17]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						, __UpperCamelCase       )       )
											# verify orig_size
											A      						=    torch.tensor([480, 640]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						, __UpperCamelCase       )       )
											# verify size
											A      						=    torch.tensor([800, 1_066]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']						, __UpperCamelCase       )       )


				@slow
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											# prepare image, target and masks_path
											A      						=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						, 'r'       ) as f:
																		A      						=    json.loads(f.read()       )

											A      						=    {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

											A      						=    pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'       )

											# encode them
											A      						=    DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'       )
											A      						=    image_processing(images=__UpperCamelCase						, annotations=__UpperCamelCase						, masks_path=__UpperCamelCase						, return_tensors='pt'       )

											# verify pixel values
											A      						=    torch.Size([1, 3, 800, 1_066]       )
											self.assertEqual(encoding['pixel_values'].shape						, __UpperCamelCase       )

											A      						=    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]       )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						, __UpperCamelCase						, atol=1e-4       )       )

											# verify area
											A      						=    torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area']						, __UpperCamelCase       )       )
											# verify boxes
											A      						=    torch.Size([6, 4]       )
											self.assertEqual(encoding['labels'][0]['boxes'].shape						, __UpperCamelCase       )
											A      						=    torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						, __UpperCamelCase						, atol=1e-3       )       )
											# verify image_id
											A      						=    torch.tensor([39_769]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						, __UpperCamelCase       )       )
											# verify is_crowd
											A      						=    torch.tensor([0, 0, 0, 0, 0, 0]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						, __UpperCamelCase       )       )
											# verify class_labels
											A      						=    torch.tensor([17, 17, 63, 75, 75, 93]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						, __UpperCamelCase       )       )
											# verify masks
											A      						=    822_873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()						, __UpperCamelCase       )
											# verify orig_size
											A      						=    torch.tensor([480, 640]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						, __UpperCamelCase       )       )
											# verify size
											A      						=    torch.tensor([800, 1_066]       )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']						, __UpperCamelCase       )       )






'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   list[tuple[int, int]]

lowerCAmelCase									=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase									=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   ):
			lowerCAmelCase  :     int       						= pos_x
			lowerCAmelCase  :     Union[str, Any]       						= pos_y
			lowerCAmelCase  :     Optional[Any]       						= (pos_y, pos_x)
			lowerCAmelCase  :     Union[str, Any]       						= goal_x
			lowerCAmelCase  :     Tuple       						= goal_y
			lowerCAmelCase  :     str       						= g_cost
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     List[Any]       						= self.calculate_heuristic()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= abs(self.pos_x - self.goal_x						)
			lowerCAmelCase  :     Any       						= abs(self.pos_y - self.goal_y						)
			return dx + dy





	def __lt__( self		,   a_						):
			return self.f_cost < other.f_cost






class 		lowerCamelCase	:



	def __init__( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= Node(start[1]		,   start[0]		,   goal[1]		,   goal[0]		,   0		,   a_						)
			lowerCAmelCase  :     Union[str, Any]       						= Node(goal[1]		,   goal[0]		,   goal[1]		,   goal[0]		,   99_999		,   a_						)

			lowerCAmelCase  :     Tuple       						= [self.start]
			lowerCAmelCase  :     list[Node]       						= []

			lowerCAmelCase  :     Tuple       						= False



	def 	_lowerCamelCase     ( self						):
			while self.open_nodes:
					# Open Nodes are sorted using __lt__
					self.open_nodes.sort()
					lowerCAmelCase  :     List[Any]       						= self.open_nodes.pop(0						)

					if current_node.pos == self.target.pos:
							lowerCAmelCase  :     Tuple       						= True
							return self.retrace_path(a_						)

					self.closed_nodes.append(a_						)
					lowerCAmelCase  :     Tuple       						= self.get_successors(a_						)

					for child_node in successors:
							if child_node in self.closed_nodes:
									continue

							if child_node not in self.open_nodes:
									self.open_nodes.append(a_						)
							else:
									# retrieve the best current path
									lowerCAmelCase  :     int       						= self.open_nodes.pop(self.open_nodes.index(a_						)						)

									if child_node.g_cost < better_node.g_cost:
											self.open_nodes.append(a_						)
									else:
											self.open_nodes.append(a_						)

			if not self.reached:
					return [self.start.pos]
			return None



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= []
			for action in delta:
					lowerCAmelCase  :     Dict       						= parent.pos_x + action[1]
					lowerCAmelCase  :     str       						= parent.pos_y + action[0]

					if not (0 <= pos_x <= len(grid[0]						) - 1 and 0 <= pos_y <= len(a_						) - 1):
							continue

					if grid[pos_y][pos_x] != 0:
							continue

					successors.append(
					    Node(
					        a_		,   a_		,   self.target.pos_y		,   self.target.pos_x		,   parent.g_cost + 1		,   a_		,   )						)
			return successors





	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= node
			lowerCAmelCase  :     List[str]       						= []
			while current_node is not None:
					path.append((current_node.pos_y, current_node.pos_x)						)
					lowerCAmelCase  :     Dict       						= current_node.parent
			path.reverse()
			return path


if __name__ == "__main__":
					lowerCAmelCase									=   (0, 0)
					lowerCAmelCase									=   (len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("""------""")

					lowerCAmelCase									=   GreedyBestFirst(init, goal)
					lowerCAmelCase									=   greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															lowerCAmelCase									=   2

										for elem in grid:
															print(elem)



